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Domain Adaptive Decision Trees: Implications for Accuracy a..:
, In:
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency
,
Alvarez, Jose M.
;
Scott, Kristen M.
;
Berendt, Bettina
. - p. 423-433 , 2023
Link:
https://dl.acm.org/doi/10.1145/3593013.3594008
RT T1 <cTitle>Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency</cTitle>: T1 <Title>Domain Adaptive Decision Trees: Implications for Accuracy and Fairness</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3594008&Exemplar=1&LAN=DE A1 Alvarez, Jose M. A1 Scott, Kristen M. A1 Berendt, Bettina A1 Ruggieri, Salvatore PB ACM YR 2023 K1 Covariate Shift K1 Decision Trees K1 Domain Adaptation K1 Fairness K1 Information Gain K1 folktables K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Classification and regression trees K1 Learning paradigms K1 Multi-task learning K1 Transfer learning K1 Learning under covariate shift SP 423 OP 433 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3593013.3594008 DO https://dl.acm.org/doi/10.1145/3593013.3594008 SF ELIB - SuUB Bremen
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